The role of gut microbiota in the gut-brain axis: current ... · microflora, or normal flora (Zimmer, 2010). Particularly, the collection of intestinal microorganisms along the GI

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2013 June 2013 | Volume 4 | Issue 6 | 403

Protein Cell 2013, 4(6): 403–414 DOI 10.1007/s13238-013-3017-x Protein Cell&

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*These authors contributed equally to the work.

The role of gut microbiota in the gut-brain axis: current challenges and perspectives

EVIEWR

Xiao Chen1*, Roshan D’Souza2*, Seong-Tshool Hong1

1 BDRD Research Institute, JINIS Biopharmaceuticals Inc, 948-9 Dunsan, Bongdong, Wanju, Chonbuk 565-902, South Korea2 Department of Microbiology and Genetics and Institute for Medical Science, Chonbuk National University Medical School, Jeonju 561-712, South Korea Correspondence: [email protected] February 20, 2013 Accepted April 1, 2013

ABSTRACT

Brain and the gastrointestinal (GI) tract are intimately con-nected to form a bidirectional neurohumoral communica-tion system. The communication between gut and brain, knows as the gut-brain axis, is so well established that the functional status of gut is always related to the condi-tion of brain. The researches on the gut-brain axis were traditionally focused on the psychological status affecting the function of the GI tract. However, recent evidences showed that gut microbiota communicates with the brain via the gut-brain axis to modulate brain development and behavioral phenotypes. These recent fi ndings on the new role of gut microbiota in the gut-brain axis implicate that gut microbiota could associate with brain functions as well as neurological diseases via the gut-brain axis. To elucidate the role of gut microbiota in the gut-brain axis, precise identification of the composition of microbes constituting gut microbiota is an essential step. However, identifi cation of microbes constituting gut microbiota has been the main technological challenge currently due to massive amount of intestinal microbes and the diffi culties in culture of gut microbes. Current methods for identifi ca-tion of microbes constituting gut microbiota are depend-ent on omics analysis methods by using advanced high tech equipment. Here, we review the association of gut microbiota with the gut-brain axis, including the pros and cons of the current high throughput methods for identi-fi cation of microbes constituting gut microbiota to eluci-date the role of gut microbiota in the gut-brain axis.

KEYWORDS gut microbiota, the gut-brain axis, central nervous system, high throughput methods, next-generation sequencings

INTRODUCTIONThe human intestine contains a massive and complex micro-bial community called gut microbiota. A typical human carries 100 trillion microbes in the body, referred to as microbiota, microflora, or normal flora (Zimmer, 2010). Particularly, the collection of intestinal microorganisms along the GI tract, gut microbiota, has been known to be essential to the health and well-being of the host. Thus, the composition of gut microbiota and its role on human health and disease became a booming area of research, presenting a new paradigm of opportunities for medical and food applications.

It has been known that the failure in functional interactions between host and gut microbiota results GI tract disorders such as infl ammatory bowel disease (IBD), GI tract malignan-cies, cholelithiasis, etc (Eckburg et al., 2005). Recent studies further revealed that the altered compositions of gut microbiota in host are with more complicated diseases such as behavior disorders and metabolic disorders. Examples are various, in-cluding autism, hepatic encephalopathy, allergy, obesity, diabe-tes, atherosclerosis, etc. Statistical analysis on the variations in the composition of gut microbiota and following biological ex-periments proved that the relationship between gut microbiota and humans is not merely commensal, but rather a mutualistic relationship. It is now clear that gut microbiota contributes sig-nifi cantly to host physiology and phenotypes such as nutritional uptake and well-being, obesity, diabetes, metabolic syndrome, behavior, various neurological diseases, etc (Bercik et al., 2012).

Gut and brain originated from the same tissue, the neural crest, during embryogenesis tightly work in tandem, each in-fl uencing the other (Cattell et al., 2011). The communication between gut and brain is called the gut-brain axis. Recent evi-dences showed that gut microbiota plays a very important role

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however, types and its frequencies of microbial organisms con-stituting gut microbiota in each individual are very different from each other at the species levels. The composition of the jut microbiota is established during the fi rst few years of life and is likely shaped by multiple factors, including maternal vertical transmission, genetic makeup, diet, antibiotics, infections, and stress (Hopkins and Sharp, 2001; Lewis and Cochrane, 2007; Cecilia and Sonja, 2010; Brian et al., 2013). Since the jut mi-crobiota is a key player of the mucosal homeostasis, dysregu-lation of the intestinal mucosa homeostasis is consequentially implicated in the progression of disorders. Extensive research-es have been undertaken over the last decade in order to link the gut microbiota and its infl uence on the host physiology and phenotypes. Particular interesting topics with high potential for personalized management of gut microbiota are treatment of metabolic syndrome and related diseases, prevention and con-trol of (recurrent) infections, immune mediated disorders, and the gut-brain axis (Wu et al., 2004; Cryan et al., 2011). The cur-rent genomic revolution offers an unprecedented opportunity to identify the molecular foundations of these relationships so that we can understand how they contribute to our normal physiol-ogy and how they can be exploited to develop new therapeutic strategies.

THE GUT-BRAIN AXISThe ability of gut microbiota to communicate with the brain and thus modulate behavior is emerging as an exciting concept in health and disease. The brain-gut axis is a bidirectional com-munication system, comprised of neural pathways, such as the enteric nervous system (ENS), vagus, sympathetic and spinal nerves, and humoral pathways, which include cytokines, hormones, and neuropeptides as signaling molecules (Crya n et al., 2011) (Fig. 1). Since the ENS is involved in the control of merely all gut functions including motility, secretion, absorption, and blood fl ow, it is an essential component of the gut-brain axis strategically placed at the interface between the gut and the brain. When the gut microbiota composition is altered driv-ing by medications, chemicals and so on, the intestinal epithe-lium cells could sense these changes and give signals to ENS by altering the hormone secretion. It is extremely receptive of chemical information arising from intestinal epithelium as well as the enteric endocrine and immune systems and provides input to sensory pathways that signal to brain areas involved in emotion and cognition (Cryan et al., 2011). On the other hand the ENS receives efferent information from the brain through autonomic neural connections (sympathetic and parasympa-thetic) and hormonal pathways, which in turn modulate diges-tive functions (Barbara et al., 2007).

In health, gut-brain interactions contribute to the regulation of digestive processes including GI motor function, secretion/absorption and blood fl ow as well as to the regulation of food intake, glucose metabolism, modulation of the gut-associated immune system and, synchronization of physical and emotion-al states impacting on the GI tract. In disease, however, altered

in the gut-brain axis to affect mental health. Also there are evi-dences that gut microbiota is associated with non-neurological conditions such as obesity, diabetes, metabolic syndrome via the gut-brain axis (Manco, 2012).

Despite of the signifi cant role of gut microbiota in the gut-brain axis, the elucidation of the molecular mechanistic path-way on the role of gut microbiota in the gut-brain axis is not moving forward as expected. This is because the elucidation of the molecular mechanistic pathway of gut microbiota in the gut-brain axis depends on precise high throughput identifi cation of the microbial organisms constituting gut microbiota. Although there were some techniques utilized to identify microbial or-ganisms constituting gut microbiota, current technological advances make the high-throughput identifi cation of individual microbial components of gut microbiota possible. Especially, the advent of next-generation sequencing (NGS) has enabled the metagenomic and meta-transcriptomic analysis for high throughput identification of microbial organisms constituting gut microbiota. In this review, we focused on the role of gut mi-crobiota in the gut-brain axis as well as the current challenges confronted in the high throughput identifi cation of microbial or-ganisms constituting gut microbiota to elucidate the role of gut microbiota in the gut-brain axis.

COMPOSITION OF GUT MICROBIOTA AND ITS INTERACTIONS WITH HOSTThe gut microbiota colonizes the GI tract immediately after birth and persists throughout the adult life. Mostly, the gut mi-crobiota is composed of anaerobic bacteria, belong to either the Bacteroidetes or the Firmicutes phylum with minor propor-tions of other phyla such as Proteobacteria, Verrucomicrobia, Actinobacteria, Fusobacteria, and Cyanobacteria. So far, mul-tiple studies suggested various numbers of bacterial species, ranging from 1200 species up to 35,000 bacterial phylotypes in the human gut. It is generally accepted that, however, the human microbiota contains as many as 100 trillion bacteria, a number that is 10 times greater than the number of human cells, representing a combined microbial genome well in ex-cess of the human genome (Bocci, 1992).

In terms of the function of host-microbe interaction, it makes sense to have outnumbered bacteria in our gut. The intestine harbors a diverse bacterial community that is separated from the internal environments by a single layer of epithelial cells. Host-microbe interaction occurs along mucosal surfaces in the intestine of host. By mutual interaction with host, gut microbiota play essential roles in providing vital functions to host, such as protection against epithelial cell injury (Rakoff et al., 2004), metabolic regulation (Stappenbeck et al., 2002), GI tract de-velopment (Stappenbeck et al., 2001), immunomodulation by development of innate and adaptive immune responses, and absorption of nutrients (Fredrik et al., 2005; Ben et al., 2008).

Considering the vast numbers of microbiota bacterial cells, it is surprising that the composition of the human microbiota is fairly stable and conserved at the phylum level. Within phyla,

Methods for identifi cation of microbes constituting gut microbiota


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&feld et al., 2011). On the other hand, the mouse experiments confi rmed that anxiety-like behavior and central neurochemi-cal changes were relieved in GF mice compared with specifi c pathogen free (SPF) mice (Desbonnet et al., 2008). In addition, tryptophan metabolism was modulated by B. infantis, suggest-ing that the normal gut microbiota can infl uence the precursor pool for serotonin which is related to neurophysiological behav-ior (Shanahan, 2002). These studies strongly suggest that gut and brain communicate each other to modulate the brain func-tion of its host through the gut-brain axis.

The gut-brain axis and behavioral phenotypes

Early life environmental influences have a profound impact on the organism’s later development, structure, and function, such as the gut microbiota (Andrew, 2002). Immediately after birth, the newborn organism is rapidly and densely populated with complex forms of indigenous microbes. This process has been shown to contribute to developmental programming of epithelial barrier function, gut homeostasis, and angiogenesis, as well as the innate and host adaptive immune function, even the common neuro-developmental disorders, such as autism and schizophrenia, and microbial pathogen infections during the perinatal period (Andrew, 2002). The infl uence of gut mi-crobiota persisted in the whole lifespan of organisms.

For gut-brain axis researches, the ideal model is the utiliza-tion of GF mouse, which are animals devoid of any bacterial contamination, offer the possibility to study the impact of the complete absence of a gut microbiota on behavior (Cryan et al., 2011). Sven Pettersson and co-workers carried out experi-ments on GF mice and SPF mice and found out the signifi cant behavioral difference between them. Comparing with the SPF mice, GF mice showed the increased motor activity and reduced anxiety-like behavior, altered expression of synaptic plasticity-related genes, elevated noradrenaline (NA), dopa-mine (DA), and 5-hydroxytryptamine (5-HT) turnover in the

gut-brain signaling and interaction are likely to underlie the pathophysiology of various eating disorders, gastroesophageal refl ux disease, nausea and vomiting, gastroparesis, functional GI disorders such as dyspepsia and irritable bowel syndrome (IBS). In addition, they might also contribute to systemic im-mune or mood disorders (Barbara et al., 2007). Therefore, the investigation of gut-brain axis could not only improve the understanding of GI disease, but also develop many potential medications for behavioral disorders and metabolic diseases, such as autism, obesity and diabetes.

The gut-brain axis and brain function

The gut-brain axis, a bidirectional neurohumoral communi-cation system in human body, was well noted traditionally because of the correlated commodity between anxiety disor-ders and IBDs (Whitehead et al., 2002; Walker et al., 2008). However, it has not been understood how gut and brain com-municate each other. Recent researches suggested that gut microbiota modulate the brain function of its host through the gut-brain axis (Turnbull et al., 1999; Finegold et al., 2002; Shanahan, 2002; Sudo et al., 2004; Desbonnet et al., 2008; Neufeld et al., 2011). The series of events in the GI tract follow-ing postnatal microbial colonization resulted a long-lasting im-pact on the neural processing of sensory information regarding the hypothalamic-pituitary-adrenal stress response (Finegold et al., 2002). Early postnatal bacterial colonization in germ-free (GF) mice promoted the development of central nervous system (CNS) (Turnbull et al., 1999). The c-Fos activation in the paraventricular nucleus was rapidly induced by the inocu-lation of B. infantis (Sudo et al., 2004). Further studies on the role of gut microbiota may clarify how and to what extent the neural and cytokine-mediated pathways can contribute to fl ora-mediated modulation of hypothalamic-pituitary–adrenal stress response. Also, Clostridial species were increased in the stools of children with autism than that in the stools of control (Neu-

Gut-brain Axis Pathways

• Neural ENS, CNS through vagal and/or spinal afferents

• Humoral Cytokines, Horm ones/ Neuropeptides, Microbial bioactive substances

Microbiota

• Immune system• Epithelial barrier function• Neurotransmission• Muscle function

Microbiota targets

Figu re 1. The gut-brain axis: Pathways of communication between brain and gut and microbiota targets. The gut microbiota al-ways infl uences the host in neural and humoral manners which interplay and connect the brain and gut as an integral axis.



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ligands for two G-protein-coupled receptors, Gpr41 and Gpr43, of gut enteroendocrine cells. Upon ligand binding, these G-protein-coupled receptors stimulate secretion of peptide YY (PYY), which inhibits gut motility and slows intestinal transit thereby enhancing nutrient absorption (Zhang et al., 2009) (Fig. 2). Besides the modulations of gut-derived peptide secre-tion, the induction of chronic low-grade endotoxinemia and regulation of tissular biologically active fatty acid composition are the other two mechanisms linking gut microbiota to obesity. All of these impacts on host can lead to the fat accumulation and body mass gain by gut-brain axis. Although the effective obesity therapeutics have been pursued for a long time, there are limited options for obese patients and the obesity pharma-cotherapy always companied with severe side-effects, such as tachycardia and fl uid derangements (Bercik et al., 2011). However, the application of gut hormones will modulate the gut composition to keep homeostasis instead of disrupting it and fi nally minimize metabolic deviations between diabetic patients and normalcy. Therefore, the gut hormones could also be a new treatment of obesity.

Diabetes is broadly considered as a metabolic disease resulting from genetic and environmental factors. Additionally, there is only less than 10% of the overall metabolic phenotype causing by point mutations and the low impact of genetics on metabolic diseases is further reinforced by the growing inci-dence of diabetes and obesity over the last decades. Since the obvious increase incidence of diabetes, especially type II diabetes, the genomic pathogen could not be the solely factor for its epidemicity. Subsequently, researches on gut microbiota of diabetic patients indicated that the proportions of phylum Fir-micutes and class Clostridia were signifi cantly reduced in the diabetic group compared to the control group. Furthermore, the ratios of Bacteroidetes to Firmicutes as well as the ratios of Bacteroides-Prevotella group to C. coccoides-E. rectale group correlated positively and signifi cantly with plasma glucose con-centration but not with body mass indexes (BMIs). Therefore, bacterial sequences, specifi c for type II diabetes rather than obesity, can be considered as signatures of hyperglycemic syndrome (Musso et al., 2010). Recently, scientists are inter-ested in treatment of faecal microbiota transplantation to pa-tients with bacterial infection after antibiotic therapy, although it remains a controversial treatment.

The gut-brain-liver axis

Glucose is the major energy source for many mammalian cells, and blood glucose levels are carefully maintained. The liver plays a major role in blood glucose homeostasis by main-taining a balance between the uptake and storage of glucose via glycogenesis and the release of glucose via glycogenolysis and gluconeogenesis. Maintaining blood glucose levels within a narrow range requires the regulation of two major meta-bolic pathways, gluconeogenesis and glycogenolysis, which produce glucose in the liver (Nordlie et al., 1999; Burcelin et al., 2011). Recently, the studies illustrated the relevance of a

striatum, which proved their hypothesis that the “healthy” gut microbiota is an integral part of the external environmental signals that modulate brain development and function (Heijtz et al., 2011). However, once the gut microbiota community is interrupted by multiple factors including maternal vertical trans-mission, genetic makeup of the individual, diet, medications such as antibiotics, GI infections and stress, the gut homeosta-sis is subsequently imbalanced and causes detrimental effects on CNS through gut-brain axis (Heijtz et al., 2011), while some other experiments concluded that the gut microbiota infl uence brain chemistry and behavior independently of the autonomic nervous system, GI-specifi c neurotransmitters, or infl ammation (Bercik et al., 2012).

On the other hand, the probiotics, which are benefi cial in the treatment of the GI symptoms of disorders, had been clinically proved the role of probiotic intervention in reducing the anxiety and stress response as well as improving mood in IBS pa-tients and those with chronic fatigue. The Lactobacillus reuteri, a potential probiotic known to modulate the immune system decreases anxiety as measured on the elevated plus maze as well as reducing the stress-induced increase of corticosterone. Although the mechanism of action is not known, some probi-otics do have the potential to lower inflammatory cytokines, decrease oxidative stress and improve nutritional status (Cryan et al., 2011).

The gut-brain axis and metabolic disease

The metabolic diseases, such as obesity, diabetes and conse-quently atherosclerotic vascular disease have become major health and public health issues worldwide. With the long-time investigation of these increasingly epidemic metabolic diseas-es, the scientists discovered that only the individual nutritional habits are not enough to explain the high incidence, especially for obesity, they started to focus on the environmental fac-tors that increase energy yield from diet, regulate peripheral metabolism and thereby cause body weight gain and insulin resistance. Extensive efforts of research revealed that the influence of gut microbiota is considered as a pivotal factor for development of these metabolic disorders. The regulatory peptide hormones, which are released from upper intestine and utilized by gut microbiota to communicate with the hypo-thalamus in brain (Bercik et al., 2011), are modulated though neural and endocrine pathways to control energy balance and glucose homeostasis, which is linked to obesity and diabetes in both human and mouse model (Migrenne et al., 2006; Sam et al., 2012).

Generally, gut microbiota synthesizes a large amount of gly-coside hydrolases that break down complex plant polysaccha-rides to monosaccharides and short-chain fatty acids (SCFA), mainly acetate, propionate, and butyrate. In obese individuals, they harbor unique H2-producing bacterial groups, particularly members of the Prevotellaceae family and certain groups with-in Firmicutes which facilitate the fermentation to produce much more SCFA (Greiner and Backhed, 2011). These SCFA are



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cies in the microbial population. Therefore, rapid and accurate identifi cation of individual microbial organisms in gut microbiota are urgently required so as to be able to elucidate the role of gut microbiota in the gut-brain axis. With recent technological developments especially in the area of NGS, it is now practi-cally possible to identify all of microbial organisms constituting gut microbiota within several days.

Next generation sequencing approaches

New technologies for accurate and raid identifi cation of bacte-ria are essential to epidemiological surveillance. For classifying and identifying bacterial species, cumbersome physiological, serological, biochemical, chemotaxonomic, and more recently genomic methods have been routinely applied in microbiology (Blaut et al., 2004). Polymerase chain reaction (PCR) based methods to detect microorganisms are available but cannot be used for classifi cation, especially in the case of unknown bac-terial samples (Sintchenko et al., 2007). DNA sequencing is one of the gold standards for the characterization of the bacte-ria, but this approach cannot be used for fast classifi cation and identifi cation. In general, these methods such as PCR require optimization for setting up specifi c assays for each bacterial strain.

To identify gut microbiome and push through the limitations encountered in the traditional culturing methods, the revolu-tionary method—NGS has been applied to achieve this target. Since the relative paucity of sequenced gene fragments and the use of fecal biota as substitute for the entire gut microbiota, that the diversity of micro-organisms is not well-defi ned could

pathway where intestinal lipids regulate glucose homeostasis involving a gut-brain-liver axis (Fig. 3). Their fi ndings indicated that the direct administration of lipids into the upper intestine increased upper intestinal long-chain fatty acyl-coenzyme A (LCFA-CoA) levels and suppressed glucose production even under the situation of sub diaphragmatic vagotomy or gut vagal deafferentation which interrupts the neural connection between the gut and the brain and blocks the ability of upper intestinal lipids to inhibit glucose production (Beraza, 2008). The dis-covery of a gut-brain-liver axis for regulating liver glucose ho-meostasis and the possible impact of insulin-resistance opens a wide variety of new therapeutic options to treat obesity and diabetes mellitus.

CURRENT HIGH THROUGHPUT METHODS FOR IDENTIFICATION OF MICROBES CONSTITUTING GUT MICROBIOTAFor elucidating the role of gut microbiota in the gut-brain axis, accurate identifi cation of microbes constituting gut microbiota is the most important premise condition. Conventionally, cul-ture-based surveys were employed to identify the human gut microbial diversity (Wang et al., 2008). However, only less than 1% of all microbial organisms in nature can be grown by cul-ture-based approaches. Also, the culture media selection and incubation condition are time-consuming. Most importantly, the experimental results based on culture-based approaches do not refl ect the population dynamics of actual microbial commu-nities because it is not possible to culture whole set of micro-bial organisms without changing frequency of individual spe-

Higher Brain Centres

PVN

ARC

POMCNPYAgRP

Food intake Food intake Gut hormones

e.g. PYY3-36GLP-1

IntestinalL-cell

Vagus nerve

Brainstem

Hypothalamus

Figure 2. Regulation of food intake by gut-brain axis: Nutrients absorbed from intestine are proposed to activate G protein coupled receptors, e.g. the L-cell. The gut hormones are released after stimulations which may infl uence food intake at three sites: the vagus nerve, brainstem and hypothalamus. Two neuronal populations are considered as critical conduits which are related to food intake modulation, the orexigenic NPY/AgRP neurons and the anorexigenic POMC neurons. Further connections of higher brain centres may control the hedonic aspects of food ingestion. ARC (arcuate nucleus), AgRP (agouti related peptide), GLP-1 (glucagon like peptide-1), NPY (neuropeptide Y), POMC (propiomelanocortin), PVN (para-ventricular nucleus), PYY (peptide YY).



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& specifi c bacterial lineages present, with a comparable degree of co-variation between adult monozygotic and dizygotic twin pairs (Turnbaugh et al., 2008). Hence, Dethlefsen et al. have investigated the distal gut microbiota communities on humans before and after the treatment of antibiotics, obtaining 7000 full length rRNA sequences and over 900,000 pyrosequencing reads from two hypervariable sequence regions of the rRNA genes, by which they have identifi es the 3300–5700 taxa from the samples they used (Dethlefsen et al., 2008). The latest de-velopments of 454 and illumina technologies offer higher reso-lution and show relative consistency with each other (Claesson et al., 2010).

Recent studies have also focused upon the “metagenomic systems biology” approach, which can potentially advance metagenomic research in the same way systems biology advanced genomics, appreciating not only the part list of sys-tem but the complex interactions among parts and impact of these interactions on functions and dynamics such as the gut-brain axis. Making use of 454 FLX-derived data in addition to illumina-dervied shotgun metagenomic data helps in validating the results obtained. Annotating the metabolic sequence data to identify the enzymatic genes reveals the metabolic reac-tions thus helps in constructing the community-level metabolic network of the gut microbiota, which has linked bacterial com-munity to the gut that is associated with the specifi c host phe-notypes (Greenblum et al., 2012). Similarly, to study the gut mi-crobiota community, Gill et al. have used whole genome short-gun sequencing and provided the relative species abundance by assembling the size and depth of the coverage of genome generated from metagenomics project (Gill et al., 2006). Fig. 4 illustrates the outline of the procedure being adopted in order to identify the individual bacteria using the NGS technology.

MALDI-TOF strategies

A further advance would be the application of matrix-associat-

reduce the precision of this method. Considering that, Eckburg et al. undertook large scale comparative analysis using full length 16S rRNA sequences to clearly characterize the adher-ent mucosal and fecal microbial communities and to examine how these microbial communities differed between subjects and mucosal sites. Consequently, 62% of the 395 bacterial phylotypes were novel and, 80% represented sequences from species that have not been cultivated (Eckburg et al., 2005) and recently more than 15% sequences has been proved from new species (Zhang et al., 2011). In succession, many studies were performed based on the full length 16S rRNA to deter-mine the extent of the bacterial diversity, 16S rRNA sequences are binned into operational taxonomic units (OTUs) according to sequence percentage sequence identity. OTUs containing sequences with >99% pairwise sequence similarity indicate “strain-level” taxa, while >97% designates “species”, >95% ID “genus”, and >90% ID “family” (Jock and Geider, 2004; Peter-son et al., 2008). Thus, full length 16S rRNA sequencing strat-egy proves signifi cant while assessing the microbial diversity in the gut microbiota.

Considering the cost in Sanger sequencing in microbial analysis, there have been several approaches through pyrose-quencing. As a cost effective and time saving technique, this has been employed in several fi elds to identify the microbial communities in various atmospheres like deep sea and soil (Sogin et al., 2006). Pyrosequencing generates large number of 16S rRNA sequence tags by amplifying the select variable regions within the 16S rRNA, also achieving 100 folds higher throughput than Sanger sequencing. Better taxonomic resolu-tion can be obtained due to targeting amplification of highly variable select regions of the gene i.e. V2, V3 or V6 regions of the 16S rRNA (Hamady, et al., 2008). Turnbaugh et al. have performed the studies on obese and lean twins (154 individu-als in total) using the pyrosequencing technology and conclud-ed that human gut microbiota is shared among family mem-bers, but that each person’s gut microbial community varies in

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NMD Areceptors

Lipids LCFA-CoA GP GP

Vagal afferentVagal afferent

Vaga

l affe

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rent

Figure 3. The gut-brain-liver axis: The glucose production in liver is modulated by upper intestine lipid absorption through gut-brain-liver axis, but not gut-liver direct interplay.



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ed laser desorption/ionization-time of fl ight mass spectrometry (MALDI-TOF MS), which provided high resolution proteomic-based comparisons of whole bacterial cells. Bacterial identi-fication based on peptidic spectra obtained by MALDI-TOF mass spectrometry was proposed >30 years ago (Anhalt and Fenselau, 1975; Claydon et al., 1996; Krishnamurthy and Ross, 1996). It has only recently been used as a rapid, inex-pensive, and accurate method for identifying bacteria. The taxonomic resolution of MALDI-TOF MS is currently consid-ered as a comparable or superior method to comparative 16S rRNA gene sequence analysis. It is recognized that the MS of different bacterial species were distinct from one another, exhibiting the potential of becoming a species discriminating characteristic, analogous to genetic fi nger printing techniques such as amplified fragment length polymorphism (AFLP) or random amplifi cation of polymorphic DNA (RAPD) (Holland et al., 1996; Welker, 2012). Use of this approach in conjunction with ongoing genomic data will greatly facilitate the recognition of gut microbiota composition. Strains giving rise to unidentifi ed proteomic profi les may be subjected to gene sequence analy-sis to promote their detailed phylogenetic characterization. This will permit a parallel updating of proteomic and genomic databases which will provide an invaluable resource for fu-ture gut ecological studies. Fig. 4 shows the general scheme of MALDI-TOF MS based identification of micro-organisms, which compares the individual MS to the reference and rules out the specifi c species in the sample. MALDI-TOF MS allows the identifi cation of various micro-organisms by so called in-tact-cell mass spectrometry and the comparison of a sample’s mass spectrum to reference mass spectra in a database. The key factors to the success of this technology are the fact that a uniform sample preparation procedure is utilized for many different types of micro-organisms, comparatively low cost and short time per analysis (Angelakis et al., 2011). Additionally, MS-based identifi cation can be readily expanded to different microbiological fi elds, including food, industrial and veterinary microbiology. Recently, MS has been implemented for bacterial identification from commercialized probiotic products. There has been discrepancy between the bacterial strain announced on the label on commercial product and strain identifi ed using MALDI-TOF. MS presented 92% specifi city compared to mo-lecular assay and additional species were identifi ed (Angelakis et al., 2011). Albesharat et al. have performed studies on mi-crobial diversity using RAPD, which was further characterized by MALDI-TOF MS with better clustering result (Albesharat et al., 2011).

Genes expressed differentially between GF mice and SPF mice. GF mice displayed increased motor activity and reduced anxiety, compared with SPF mice with normal gut microbiota (Heijtz et al., 2011). However, there needs to be additional studies required in analyzing the direct role of the gut micro-biota influencing the gut-brain axis. Animal-based research has extended the idea of microbiota-brain interactions to many psychiatric disorders and brain development. Studies indicate

that behavioral changes induced by destabilization of the microbiota are likely to be mediated by substances of micro-bial origin acting on the host brain either directly or indirectly through neuro-active substances (Heijtz et al., 2011). However, it is still under debate, whether the gut microbiota metabolites are contributing towards the neural behavior or not. Tradition-ally, MALDI-TOF approach involved culturing the bacteria, followed by its spectrum analysis. Recent studies suggest that feces samples have been processed and directly fed to the instrument for analysis, by which the non-culturable bacteria can also be identifi ed and help elucidating the gut-brain axis. Moreover, the MS approach presented allows the integration of data from different biological levels such as the genome and the proteome. It used protein mass pattern detection ap-proach which is independent from DNA sequencing or PCR-based approaches (Collins et al., 2012). Lagier et al. analyzed 32,500 colonies by MALDI-TOF, found 340 different bacterial species among seven phyla and 117 genera. This included 174 species never described in the human gut (Lagier et al., 2012). However, most of these procedures have so far not ex-ceeded proof-of-principle level and were applied only to a lim-ited number of bacterial species (Holland et al., 1996; Chong et al., 1997). In addition, these procedures do not have proven maturity for easy and systematic application in microbiology. Consequently, biologists have not consistently utilized these approaches despite their great potential.

Recently, stable isotope probing approaches have been introduced, offering the great potential to identify microbes that are involved in the metabolism of specifi c substrates. Stable isotopes like 13C or radioisotopes like 14C are added to the samples and monitored for the phylogenetic information (Du-mont et al., 2006). Raman microspectroscopy (Huang et al., 2007) and nano-secondary ion mass spectrometry (Kuypers and Jørgensen, 2007) also have been used which combine the single cell technologies with stable isotope analysis of mi-crobial communities to monitor the single-cell level. In addition, Raman approach can be combined with fl uorescence in situ hybridization (FISH) which facilitates the understanding the link between individual bacterial cells and their metabolic functions which will be of great signifi cance in order to link the gut-brain axis (Dumont et al., 2006). However, this technology is far from becoming commonplace and affordable, because of the high cost and infrastructure required for the analysis.

Fluorescence in situ hybridization

The principle of FISH is the detection of a target DNA or RNA site by a fl uorescently labeled probe molecule. The high sensi-tivity and specifi city of FISH and the speed with which the as-says can be performed have made FISH a powerful technique with numerous applications, and it has gained general accept-ance as a clinical laboratory tool (Bishop, 2010).

Salminen and colleagues used this method to work on the gut microbiota composition in the fi rst and third trimesters of pregnancy. They selected 36 normal pregnant women and 18



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overweight pregnant women for experiment and found that the gut microbiota composition changed during pregnancy in nor-mal-weight and overweight women according to weight gain over pregnancy (Collado et al. 2008). Certainly, the corrected probe design in these experiments is the guarantee of specifi c detection of gut microbiota (Kurz et al., 2011). As the analysis of gut microbiota, the knowledge of the 16S rRNA sequence information is imposed to design a probe that specifi cally tar-gets a given organism. However, in silico designed probes may not work well in in situ hybridisation experiments which could reduce the accuracy of identifi cation of gut microbiota.

DGGE/TGGEDenaturing gradient gel electrophoresis (DGGE) works by ap-plying a small sample of DNA (or RNA) to an electrophoresis gel that contains a denaturing agent. Researchers have found that certain denaturing gels are capable of inducing DNA to melt at various stages. As a result of this melting, the DNA spreads through the gel and can be analyzed for single com-ponents, even those as small as 200 –700 base pairs. TGGE, a refi nement of it, relies on temperature dependent changes in structure to separate nucleic acids. TGGE and DGGE can be applied to nucleic acids such as DNA and RNA, and (less commonly) proteins. They have been used to assess microbial community diversity in sludge, sub-seafloor biosphere, soil, biofi lm, river, wastewater, crude oil, human intestines, insects, manure, probiotic products, cheese, milk, and fermented sau-sage (Wook et al., 2011). Recently, PCR-DGGE method is ap-plied to detect bacterial partial 16S rRNA gene amplicons from

ruminal fluid, aiming at the research of host feed efficiency under a low-energy diet for the fi rst time. They found that PCR-DGGE bands represented specifi c bacteria to metabolites in the bovine rumen and linked with host feed effi ciency traits, although the exact strain has not been fi gured out due to the limitations of the existing database (Hernandez et al., 2010).

Microarray

Microarray has been developed and validated for determining the microbiota diversity and evaluating the relative proportions of genus-like or higher (phylum-like) phylogenetic groups (Kim et al., 2005; Lotta et al., 2013). The fi rst extensive DNA micro-arrays containing probes designed to detect members of the GI microbiota were developed in the Brown laboratory, which is based on an Agilent platform containing probes targeting up to 359 microbiota species, as well as up to 316 “novel OTUs” identifi ed during studies of human colon and stomach microbial ecology (Palmer et al., 2006; Palmer et al., 2007). Microarrays represent an excellent choice for the high-throughput analysis of bacterial populations, because many different probes can be placed on one slide or chip, and samples thus can be tested for the presence of many different species simultaneously samples can be interrogated directly, circumventing any need for culturing, and thus non-culturable species can be reliably detected. It’s been demonstrated that microarray has a power equal to new generation sequencing (Claesson et al., 2009).

Several types of microarrays have been used to identify the gut microorganisms, like community genome array, functional genome array and phylogenetic oligonucleotide array (Loy et

Figure 4. Schematic representation of the bacterial identifi cation using NGS and MALDI-TOF platform: Genomic DNA is extract-ed from feces and analyzed using NGS and the sequence reads are compared with the database. Samples are directly fed into the MALDI-TOF, followed by its spectrum analysis to identify the bacteria present.

Collectfeces

Collectfeces

IntestineGenomic DNAextraction

NGS analysis

Sequence analysis

L. acidophilusS. aureusE. coliL. acidophilus

S. aureusE. coli

E. feacalis

Individual bacterialidentification

Spectrum analysisSamples fed directlyinto MALDI-ToF

m/z



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the GI tract obtained by using an enteroscope would answer the question on the role of site-specifi c intestinal microbiota in the gut-brain axis.

ACKNOWLEDGMENTS

This research was supported by “Ministry of Knowledge Economy (MKE), Korea Institute for Advancement of Technology (KIAT) and Honam Leading Industry Offi ce through the Leading Industry Develop-ment for Economic Region”.

ABBREVIATIONS

5-HT, 5-hydroxytryptamine; AFLP, amplifi ed fragment length polymor-phism; BMI, body mass index; CNS, central nervous system; DA, dopamine; DGGE, denaturing gradient gel electrophoresis; FISH, fl uo-rescence in situ hybridization; GF, germ-free; GI, gastrointestinal; IBD, inflammatory bowel disease; IBS, irritable bowel syndrome; LCFA-CoA, long-chain fatty acyl-coenzyme A; MALDI-TOF MS, matrix-asso-ciated laser desorption/ionization-time of fl ight mass spectrometry; NA, noradrenaline; NGS, Next-generation sequencing; OUT, operational taxonomic unit; RAPD, random amplification of polymorphic DNA; PYY, peptide YY; SCFA, short-chain fatty acid; SPF, specifi c pathogen free

COMPLIANCE WITH ETHICS GUIDELINES

Xiao Chen, Roshan D’Souza and Seong-Tshool Hong declare that they have no confl ict of interest.

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CONCLUSION AND PROSPECTThe concept of gut microbiota as a determining factor for the various human phenotypes ranging from obesity to intelligence is one of the most noticed research topics in current sciences. Especially, the recent years are witnessing that gut microbiota regulate the human phenotypes through the gut-brain axis. Studies are revealing that gut microbiota seems to communi-cate with CNS through neural, endocrine and immune path-ways to infl uence the physiology of body including the brain functions and behaviors of its host (Cryan and Dinan, 2012). However, clear elucidation of the role of gut microbiota in the gut-brain axis is confronted with a technological challenge of identifying microbes constituting the gut microbiota. The gut mi-crobiota of a typical human is consisted of 100 trillion microbes, a number that is 10 times greater than the number of human cells (Zimmer, 2010). Although there are the pros and cons for current high throughput identifi cation methods for identifi cation of microbes constituting gut microbiota, NGS methods are the best approaches. After isolating whole genomic DNAs from a fecal sample, the high throughput identifi cation of 16S rDNA by NGS alone generates enough information for high throughput identifi cation of microbes constituting gut microbiota. Further computer-assisted statistical data analyses on the composi-tion of gut microbiota, information of body phenotypes, and the status of the gut-brain axis will elucidate how gut microbiota af-fect and regulate the phenotypes of mammals through the gut-brain axis.

Finally, there is a possibility that the site-specifi c intestinal microbiota would affect the gut-brain axis. It has been well known that different microbes are enriched at the different sites of the GI tract ( Simren et al., 2012). This result suggests that a different microbial composition at a specifi c location in the GI tract affect differently the phenotypes of host, because each locus of the GI tract not only functions differently but also up-take a different kind of nutrients (Sekirov et al., 2010). It would be diffi cult to answer whether the site-specifi c gut microbiota play a determinant role in the gut-brain axis. Intestinal microbe analyses on the fecal and tissue sample at the different sites of



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The role of gut microbiota in the gut-brain axis: current ... · microflora, or normal flora (Zimmer, 2010). Particularly, the collection of intestinal microorganisms along the GI